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Data-driven Classification of Screwdriving Operations

Reuben Aronson, Ankit Bhatia, Zhenzhong Jia, Mathieu Guillame-Bert, David Bourne, Artur W. Dubrawski and Matthew T. Mason
Conference Paper, International Symposium on Experimental Robotics, October, 2016

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Abstract

Consumer electronic devices are made by the millions, and automating their production is a key manufacturing challenge. Fastening machine screws is among the most difficult components of this challenge. To accomplish this task with sufficient robustness for industry, detecting and recovering from failure is essential. We have built a robotic screw- driving system to collect data on this process. Using it, we collected data on 1862 screwdriving runs, each consisting of force, torque, motor current and speed, and video. Each run is also hand-labeled with the stages of screwdriving and the result of the run. We identify several dis- tinct stages through which the system transitions and relate sequences of stages to characteristic failure modes. In addition, we explore several techniques for automatic result classification, including standard maxi- mum angle/torque methods and machine learning time series techniques.

BibTeX Reference
@conference{Aronson-2016-5619,
title = {Data-driven Classification of Screwdriving Operations},
author = {Reuben Aronson and Ankit Bhatia and Zhenzhong Jia and Mathieu Guillame-Bert and David Bourne and Artur W. Dubrawski and Matthew T. Mason},
booktitle = {International Symposium on Experimental Robotics},
month = {October},
year = {2016},
}
2017-09-13T10:38:12+00:00